alt Hacker NewsYou can find the 4 versions of Benedict's deck here: https://www.ben-evans.com/presentations I appreciate the temporal view into this thinking. My interpretation:
Nov 2024: Don’t dismiss this; it may be the next platform shift. But the actual questions are still unsettled: scaling, usefulness, deployment, and business model.
May 2025: The model layer is already showing signs of commoditization, so the important question shifts toward deployment: products, use cases, UX, errors, and enterprise adoption.
Nov 2025: The capital cycle has become the story: everyone is spending because missing the platform shift is worse than overbuilding, but there is still no clarity on product shape, moats, or value capture. That creates bubble-like dynamics.
May 2026: Provisional thesis: models look likely to become infrastructure, while value probably moves up-stack into apps, workflows, product, proprietary data/context, GTM, and new questions made possible by cheap automation. But he is still explicitly calling this provisional.
Replies
I think that DeepSeek may be important to that. They have a really good model that's open source, raising the bar for all other players: how good your model needs to be so you can make meaningful money on it (better than DeepSeek).
Same thing happened on other places the open source offering became popular.
I didn’t know there were a sequence of these decks; thanks — it’s helpful to think of them as updating snapshots in time.
The main thing that stands out to me on these graphs is just . how . early we still are - looking at industries like legal which in my mind are certainly going to be massively disrupted, and seeing the very low usage rates vs. tech (which still shows less than a quarter of tech people using AI daily) — we are in for a lot more change than we’ve seen so far.
Well, yes. Anyone who tells you they know how this is going to work is an idiot.
I appreciate Evans’ work and wrote an “antithesis” to the Nov 2024 iteration of this. Given the pivot to “models look likely to become infrastructure” I might want to update my take.
Thanks for the summary. I do love Benedict‘s work; I find he’s one of the few commentators who consistently strikes a balance between taking the transformative potential of AI seriously while not falling over into hype.
Some things that stand out:
* He’s really good with his historical analogies, especially looking at previous transformations like the early Internet and mobile; no surprise given that he has a history degree.
* he emphasizes over and over how we have still have no idea how all of this is going to work when the dust settles. I think that’s kind of a historian’s move as well. When you look at what people were saying during the early days of the web, for example, almost all of their predictions weren’t just wrong… in hindsight, given how the future played out, they were asking the wrong questions. The implication is that we are probably asking the wrong questions about AI too.
* Nonetheless his thesis about the commoditization of models is actually a fairly strong concrete prediction. i’m not sure if I agree with it entirely, but I do keep it in mind every time I look at the valuation of leading AI labs.
* he continually makes the point that a chat bot is barely a product and that AI labs have so far had very little success in delivering products above that layer… with the exception of coding agents, of course.